Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fabioc-aloha/Alex_Skill_Mall --skill prompt-buildergit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/prompt-builder)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/prompt-builder"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/prompt-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/prompt-builder"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/prompt-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.01281 |
| Opus 5 | $0.00009 | $0.00641 |
| Sonnet 5 | $0.00004 | $0.00256 |
| Haiku 4.5 | $0.00002 | $0.00128 |
Grade A, and why
prompt-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Builder
Build
.prompt.mdfiles that pass brain-qa on first attempt — no regressions
When to Use
- Creating a new
.prompt.mdfile - Fixing a failing prompt in brain-qa
- Adding prompts to the loop menu config
- Reviewing prompt frontmatter for completeness
Prompt Anatomy
Every .prompt.md has two parts: YAML frontmatter and Markdown body.
Frontmatter (Required Fields)
| Field | Required | Gate | Purpose |
|---|---|---|---|
description |
Yes | Yes | What the prompt does (1 sentence) |
application |
Yes | Yes | When/why to use it (distinct from description) |
mode |
Conditional | No | Set to agent for loop prompts |
agent |
Conditional | No | Named agent for root prompts |
tools |
Conditional | No | Tool array for loop prompts |
Gate fields (description + application) are mandatory — brain-qa fails without both.
brain-qa Scoring (4 points)
| Flag | Points | Rule |
|---|---|---|
desc |
1 | description: exists in frontmatter |
app |
1 | application: exists in frontmatter |
agent |
1 | agent: OR mode: agent exists |
>20L |
1 | Body exceeds 20 lines |
Pass = both gates (desc + app) AND score >= 3.
Two Archetypes
Root Prompts (.github/prompts/*.prompt.md)
Invoked directly by name. Typically route to a named agent.
---
description: "What this prompt does"
application: "When to use this prompt"
agent: the AI assistant
---
Key rules:
- Use
agent:to name the target agent (the AI assistant, Researcher, Validator, etc.) - Do NOT use
mode:ortools:— those are for loop prompts - Body should be 20+ lines with clear instructions
Loop Prompts (.github/prompts/loop/*.prompt.md)
Sidebar buttons in the extension Loop tab. Loaded by loopMenu.ts.
---
mode: agent
description: "What this prompt does"
application: "When to use this prompt"
tools: ["read_file", "create_file", "run_in_terminal"]
---
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 185 lines · 18 tokens per session scan A a66f52cec190
prompt-builder is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,281 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
vendor-prompting
ANALYSIS SKILL — Audit-grade reference for Anthropic Claude and OpenAI GPT-5.6 prompting best practices. WHEN: "claude prompting", "gpt-5.6 prompting", "audit agent", "review prompt", "vendor best practices", "anthropic best practices", "openai prompting". DO NOT USE FOR: routine prompt edits where rules are already…
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.
ai-observability-promptfoo
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation.
midjourney-prompter
Engineer Midjourney prompts — style references, aspect ratios, negative prompts, and v6 parameter tuning.
stable-diffusion-helper
Craft Stable Diffusion prompts — SDXL, LoRA triggers, ControlNet hints, and ComfyUI workflow design.
model-recommendation
Analyse chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency.